Automated Methods for Detecting Volcanic Deformation Using Sentinel‐1 InSAR Time Series Illustrated by the 2017–2018 Unrest at Agung, Indonesia
Author:
Affiliation:
1. COMET, School of Earth SciencesUniversity of Bristol Bristol UK
2. COMET, School of EngineeringNewcastle University Newcastle upon Tyne UK
Funder
Natural Environment Research Council
Publisher
American Geophysical Union (AGU)
Subject
Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Geochemistry and Petrology,Geophysics
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019JB017908
Reference62 articles.
1. Dyke intrusion between neighbouring arc volcanoes responsible for 2017 pre-eruptive seismic swarm at Agung
2. A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets
3. Application of Machine Learning to Classification of Volcanic Deformation in Routinely Generated InSAR Data
4. Volcano-wide fringes in ERS synthetic aperture radar interferograms of Etna (1992-1998): Deformation or tropospheric effect?
5. Andean structural control on interseismic coupling in the North Chile subduction zone
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